News – 1000FARMS

08 May 2024

Call for proposals: 1000FARMS catalytic small projects 2024

The call for catalytic small projects supports learning about tricot & implementation of the approach in a low-risk way. Find out more and apply.

Credit: CIAT / Georgina Smith

10 July 2023

Call for proposals: 1000FARMS catalytic small projects 2023

1000FARMS has funding available to support 5–6 catalytic projects in 2023 for up to a maximum of USD 20,000. Find out more and how to apply.

Credit: ICRISAT

10 July 2023

Climate variability indices for ecological and crop models in R: the climatrends package

The package climatrends aims to provide the methods in R to compute precipitation and temperature indices that serve as input for climate and crop models, trends in climate change and applied ecology.

26 June 2023

Establishing the linkage between eba’s instrumental and sensory descriptive profiles and their correlation with consumer preferences: implications for cassava breeding

This study aimed to define the critical quality traits of gari and eba, measure their heritability, define medium and high throughput instrumental methods for use by breeders, and link the traits with consumer preferences.

12 June 2023

Data-driven approaches can harness crop diversity to address heterogeneous needs for breeding products

This perspective describes the opportunities and challenges of data-driven approaches for crop diversity management (genebanks and breeding) in the context of agricultural research for sustainable development in the Global South.

Credit: Neil Palmer / CIAT

30 May 2023

gosset: An R package for analysis and synthesis of ranking data in agricultural experimentation

To derive insights from data, researchers working on agricultural experiments need appropriate data management and analysis tools. To ensure that workflows are reproducible and can be applied on a routine basis, programmatic tools are needed. Such tools are increasingly necessary for rank-based data, a type of data that is generated in on-farm experimentation and data […]